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Eighteen months ago, I viewed the near-term technological feasibility of self-driving cars as a largely unsolvable mystery. More recently, companies like Waymo, Cruise, and Zoox have given (direct or indirect) indications that their autonomous vehicles are either superhuman or fast approaching it in certain environments. This is one reason my confidence has been slowly growing.

Another reason is that there have been fundamental advances in AI for each of the three major subdomains of self-driving car AI: perception, prediction, and planning.

In perception, the main advance is self-supervised learning on unlabelled video. This means using part of a video to predict a probability distribution for another part of a video. Neural networks trained this way automatically learn representations of objects that can later be fine-tuned with a labelled dataset. The combination of unlabelled and labelled data is called semi-supervised learning.

Self-supervised pre-training scales with data and compute. This means vastly more data can be made useful than would ever be economically possible under a purely hand-labelled paradigm.

A wildcard that Tesla is apparently working on is 3D labelling. It isn’t 100% clear to me how 3D labelling is meant to work, but the speculation that makes the most sense to me is that Tesla is using classical photogrammetry to reconstruct the 3D scene. What human labellers will see is the 3D reconstruction, not the raw images. Elon Musk claims this will enable Tesla to leverage human labour orders of magnitude more efficiently, since labelling a single 3D object creates labels for many 2D video frames.

In prediction, the problem is that the fundamental work required to turn the prediction of future behaviours of road users into a deep learning problem is at an earlier stage than computer vision. That's the troubling part. The hopeful part is that it seems like prediction is getting more love now. With prediction, as opposed to computer vision, it is easier to make the process completely self-supervised. The future itself provides unlimited ground truth labels. Labour is no bottleneck.

In planning, the advance that excites me is imitation learning. AlphaStar, an expert-level StarCraft AI by DeepMind, is an astonishing proof of concept of imitation learning. Simply observe many, many, many instances of human behaviour and learn the correlations between the state of the environment and the actions humans take.

Recent research hopes to take this further, with imitative agents that can learn causation, as opposed to just correlation. In the autonomous vehicle domain, hybrid systems can also be created that fall back on hand-coded planners when the present situation falls outside the scope of the imitative agent’s training dataset.

Ideas like self-supervised video prediction, fully machine learned prediction, and causal imitation learning appear like they may be powerful enough to push self-driving cars across the finish line, especially if existing prototypes are already beating human performance on some metrics in some environments.

Tesla will soon be bringing Level 2 semi-automated urban driving features to its fleet. Tesla is working on most if not all of the ideas mentioned in this newsletter and it also benefits from 100x more fleet data than any other company. Tesla’s only “disadvantage” relative to others is its eschewal of lidar. But, in principal, nothing is stopping Tesla from using lidar for dedicated robotaxis, regardless of what it does with its consumer vehicles. I continue to believe that the stock market will be blindsided by what happens in the next 1-2 years.

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In an interview released on Friday, Jesse Levinson, the Chief Technology Officer of the self-driving car startup Zoox, made a remarkable comment about the capabilities of Zoox’s autonomous vehicles:

...we also measure human driving. So, we’ve had humans drive a lot of those same really challenging routes and we measure when humans make mistakes. And what's pretty exciting is a few months ago we got to the point where our AI system is making fewer mistakes than people do on those routes.

Levinson also said that Zoox’s goal is to achieve a rate of at-fault crashes “about an order of magnitude lower than it is for humans.” The company aspires to deploy a driverless vehicle without a steering wheel or pedals by the end of 2021. This implies Zoox hopes to reach significantly superhuman safety by the end of next year.

It’s difficult for me to accept on trust Levinson’s comment about the error rate for human driving vs. AI driving. As a techno-optimist and robotaxi investor, I’m tempted to believe that Zoox is perhaps the second company to pass this major milestone. But I would be a lot more convinced if I knew the metrics Zoox is using to measure safety and the sample size of miles driven.

The only public data we have on self-driving cars is the rate of safety driver disengagements of the autonomous system. Cruise President and CTO Kyle Vogt published a blog post in January that convincingly argued that disnegagements are not a good metric for safety or for apples-to-apples comparisons with human beings. For better insight into how close or far self-driving cars really are to human capability, we need companies to open up about their testing methodologies and what metrics they’re using internally. And, of course, what numbers they're actually getting.

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This week, Waymo raised $2.25 billion from outside investors led by the private equity firm Silver Lake, the Canada Pension Plan Investment Board, and Mubadala, a sovereign wealth fund of the United Arab Emirates. Additional investors included the venture capital firm Andreessen Horowitz, the car dealer AutoNation, the contract carmaker and auto parts maker Magna, and Waymo’s parent company Alphabet.

According to reporter Richard Waters at The Financial Times, the investment round valued Waymo at $30 billion. By comparison, Cruise, a subsidiary of General Motors, is valued at $19 billion by its outside investors, which include SoftBank, Honda, and T. Rowe Price.

It seems increasingly likely to me that Waymo has internal metrics that show its driverless robotaxis in the Phoenix, Arizona metro area have superhuman safety. Hopefully, Waymo will be able to publicly confirm this hunch by the end of 2020:

Cruise, for its part, seems confident it can cross the human-level threshold within the next few years:

The last concrete information we got out of Cruise was an internal report that was leaked to the press in June. The report projected (it’s unclear on what basis) that Cruise would be at 5% to 11% of human-level safety by the end of 2019.

The upshot is that robotaxi companies that are perceived to be global leaders are getting valuations of ~$20 billion or $30 billion, even given the uncertainty, skepticism, and sense of risk that pervades today. Here are what I see as the next steps for robotaxi companies:

Show a positive gross margin that demonstrates a path to long-term net profitability.

Devise a credible plan to rapidly scale up service to the level of nations and continents.

If those four criteria are satisfied, then I believe we’re looking at a scenario where the global robotaxi market is worth $1 trillion+ collectively, as the equity research firm ARK Invest models:

Autonomous cars are, of course, robots that use deep learning: to perceive the environment, to predict the future, and to plan actions. The performance of deep learning scales with data, sometimes in predictable, lawlike ways. Baidu conducted research that found, for image recognition, accuracy scales roughly 2x with each 10x increase in data. So, let’s use this knowledge to do a comparison between companies.

In October 2018, Waymo announced its fleet had driven 10 million miles cumulatively. Fourteen months later, in January 2020, it announced it hit 20 million miles. That’s 715,000 miles per month.

Tesla has a fleet of roughly 800,000 cars equipped with 360-degree cameras, a forward-facing radar, ultrasonics, and either a) the “Hardware 2” computer supplied by Nvidia or b) the ~10-20x more powerful Full Self-Driving Computer designed in-house by Tesla (also known as “Hardware 3”). My rough guess is that approximately 400,000 cars have the FSD Computer. Let’s assume these 400,000 cars drive 37 miles per day on average. That’s 440 million miles per month or about 620x more than Waymo. Including all 800,000 cars, Tesla’s drives over 1,200x as much as Waymo.

With the scaling rate discovered by Baidu, 620x more data would translate into about 6x better performance (if my math is correct.). 1,200x more data would rest in more than 8x better performance. In my opinion, because investors and analysts don’t appreciate this fact, Tesla is radically mispriced as a robotaxi company relative to Waymo and Cruise. I could be wrong, but as far as I know, investors and analysts broadly attribute almost $0 in value to Tesla as a robotaxi company. (Please email me if you think this might be incorrect.)

It’s true that for what’s known as fully supervised deep learning of computer vision tasks, the bottleneck is manual labelling, rather than miles driven. However, this is not true for self-supervised, semi-supervised, or weakly supervised learning of computer vision tasks. It’s also not true for prediction tasks or planning tasks at all. Moreover, the quality of data used in fully supervised learning scales with miles driven. Companies employ a variety of techniques to automatically curate the most valuable data from their fleets. The more miles driven, the more value. (See an elaboration on all these concepts in my blog post here.)

For example, consider a rare type of wildlife like moose or bears. Or a rare vehicle type like an excavator or tanker truck. A fleet of cars that drives 620x more will encounter 620x more moose, bears, excavators, and tanker trucks. If these objects are rare enough that the bottleneck is finding enough new examples to label, then Tesla’s performance on object recognition for these rare objects will scale with its miles driven. 620x more examples will lead to 6x better performance.

As I see it, in their pricing of Waymo, Cruise, and Tesla, the markets are neglecting a fact of computer science. I think the narrative around Tesla and autonomy will profoundly shift once Tesla finishes its rewrite of Autopilot and ships it to customers. I expect that will most likely happen before the end of this year. The underlying computer science principles, which remain unappreciated by market participants, will translate into visible progress in the production Autopilot system that is used by hundreds of thousands of customers. At that point, I suspect many of Wall Street’s sell-side analysts will scramble to update their views and begin citing Tesla’s data advantage.

Financial disclosure: I own shares of Tesla (TSLA).

Important disclaimer: This newsletter is not intended as financial advice. Invest at your own risk and please consult a professional investment advisor if that is appropriate to your situation.

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Wonderous robotic futures await us. Deep learning is coming alive in real robot bodies, whether they are self-driving cars or robot arms in warehouses. This newsletter will be my stream of consciousness as I explore what’s at the edge of possibility.